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        <h1>DeepSeek Coder - 学习卡片</h1>
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    <div class="card-container" onclick="this.classList.toggle('flipped');">
      <div class="card">
        <div class="card-face card-front">
          <div class="card-category">技术</div>
          <div class="card-question">DeepSeek-Coder-V2 采用的核心技术架构是什么？这种架构如何提升其效率？</div>
          <div class="card-footer">点击卡片查看答案</div>
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          <div class="card-category">技术</div>
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            <div class="card-answer">它采用混合专家模型（MoE）架构。该架构将模型拆分为多个专注于特定任务的专家模块，从而显著提升了推理效率与灵活性。</div>
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          <div class="card-source">来源: 1.1 技术架构创新</div>
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          <div class="card-category">特性</div>
          <div class="card-question">DeepSeek-Coder-V2 支持的最大上下文窗口是多少？这一特性对大型项目开发有何帮助？</div>
          <div class="card-footer">点击卡片查看答案</div>
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          <div class="card-category">特性</div>
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            <div class="card-answer">上下文窗口扩展至 128K tokens。这使得模型能够支持跨文件的代码理解与生成，非常适用于大型项目的开发场景。</div>
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          <div class="card-source">来源: 1.1 技术架构创新</div>
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    <div class="card-container" onclick="this.classList.toggle('flipped');">
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          <div class="card-category">性能</div>
          <div class="card-question">在代码生成和数学推理方面，DeepSeek-Coder-V2 的性能与哪些主流模型相当或超越？</div>
          <div class="card-footer">点击卡片查看答案</div>
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          <div class="card-category">性能</div>
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            <div class="card-answer">在代码生成能力上，它与 GPT-4-Turbo 相当；而在数学推理能力上，它超越了 Claude 3 Opus 和 Gemini 1.5 Pro。</div>
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          <div class="card-source">来源: 1.2 性能优势</div>
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          <div class="card-category">配置</div>
          <div class="card-question">部署 DeepSeek-Coder-V2 的最低硬件要求是什么？</div>
          <div class="card-footer">点击卡片查看答案</div>
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          <div class="card-category">配置</div>
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            <div class="card-answer">最低硬件要求包括：1颗 Xeon Gold 64核 CPU，2块 NVIDIA A30 GPU，256GB 内存，以及 250GB SSD 存储。</div>
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          <div class="card-source">来源: 2.1 硬件要求</div>
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          <div class="card-category">优化</div>
          <div class="card-question">文档中提到了哪种工具可以加速 DeepSeek-Coder-V2 的本地推理？预计能带来多大的性能提升？</div>
          <div class="card-footer">点击卡片查看答案</div>
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          <div class="card-category">优化</div>
          <div class="card-answer-wrapper">
            <div class="card-answer">推荐使用 vLLM 来加速推理。根据文档，使用 vLLM 可以将吞吐量提升 3-5 倍。</div>
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          <div class="card-source">来源: 2.2 安装步骤</div>
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          <div class="card-category">技术</div>
          <div class="card-question">什么是“思维链（CoT）”提示词工程技术？在什么场景下推荐使用它？</div>
          <div class="card-footer">点击卡片查看答案</div>
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          <div class="card-category">技术</div>
          <div class="card-answer-wrapper">
            <div class="card-answer">思维链（CoT）是一种通过在提示中加入“请分步骤思考后生成代码”等指令来引导模型进行逐步思考的技术。它被推荐用于处理复杂任务，以提升代码生成的质量。</div>
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          <div class="card-source">来源: 4.1 提示词工程</div>
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          <div class="card-category">优化</div>
          <div class="card-question">如何通过量化压缩来减少 DeepSeek-Coder-V2 的显存占用？效果如何？</div>
          <div class="card-footer">点击卡片查看答案</div>
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          <div class="card-category">优化</div>
          <div class="card-answer-wrapper">
            <div class="card-answer">可以使用 `bitsandbytes` 库进行 8-bit 量化，具体操作是在加载模型时设置 `load_in_8bit=True` 参数。这种方法可以使显存占用减少约 40%。</div>
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          <div class="card-source">来源: 4.2 性能调优</div>
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          <div class="card-category">机制</div>
          <div class="card-question">DeepSeek-Coder-V2-Lite-Instruct 模型的总参数量和活跃参数量分别是多少？</div>
          <div class="card-footer">点击卡片查看答案</div>
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          <div class="card-category">机制</div>
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            <div class="card-answer">该模型的总参数量为 16B（160亿），而由于采用了 MoE 架构，其活跃参数量仅为 2.4B（24亿）。</div>
          </div>
          <div class="card-source">来源: 六、模型下载</div>
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        <div class="card-face card-front">
          <div class="card-category">特性</div>
          <div class="card-question">DeepSeek-Coder-V2 如何实现多模态功能，例如将UML图转换为代码？</div>
          <div class="card-footer">点击卡片查看答案</div>
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          <div class="card-category">特性</div>
          <div class="card-answer-wrapper">
            <div class="card-answer">通过与 DeepSeek-VL2 模型联动，可以实现多模态扩展。这种结合支持动态分辨率和图像理解，从而能够实现图像到代码的转换，例如将 UML 图转化为类结构代码。</div>
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          <div class="card-source">来源: 5.2 多模态扩展</div>
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